Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for geeglm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, ...)

Arguments

x

A geeglm object returned from a call to geepack::geeglm().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

quick

Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. Defaults to FALSE.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

If conf.int = TRUE, the confidence interval is computed with the an internal confint.geeglm() function.

If you have missing values in your model data, you may need to refit the model with na.action = na.exclude or deal with the missingness in the data beforehand.

See also

Value

A tibble::tibble() with columns:

regresion

TRUE

Examples

library(geepack) data(state) ds <- data.frame(state.region, state.x77) geefit <- geeglm(Income ~ Frost + Murder, id = state.region, data = ds, family = gaussian, corstr = "exchangeable") tidy(geefit)
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 4406. 407. 117. 0 #> 2 Frost 1.69 2.25 0.562 0.453 #> 3 Murder -22.7 31.4 0.522 0.470
tidy(geefit, quick = TRUE)
#> # A tibble: 3 x 2 #> term estimate #> <chr> <dbl> #> 1 (Intercept) 4406. #> 2 Frost 1.69 #> 3 Murder -22.7
tidy(geefit, conf.int = TRUE)
#> # A tibble: 3 x 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 4406. 407. 117. 0 3608. 5205. #> 2 Frost 1.69 2.25 0.562 0.453 -2.72 6.10 #> 3 Murder -22.7 31.4 0.522 0.470 -84.2 38.8